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Chin-Teng Lin, Chun-Hsiang Chuang, Yu-Chia Hung

IEEE Transactions on Cybernetics

|August 21, 2020

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Predicting driver fatigue is crucial for road safety. A novel 4-D convolutional neural network (4-D CNN) effectively analyzes electroencephalography (EEG) signals to forecast cognitive states and enhance driver performance monitoring.

Area of Science:

Neuroscience
Machine Learning
Transportation Safety

Background:

Vehicle accidents are a leading global cause of fatalities, often resulting from driver fatigue-induced errors.
Predicting driver fatigue is essential for preventing accidents and improving road safety.
Electroencephalography (EEG) is a valuable tool for monitoring brain states and behaviors.

Purpose of the Study:

To develop and evaluate a novel deep learning algorithm for predicting driver fatigue using EEG signals.
To assess the performance of a 4-dimensional convolutional neural network (4-D CNN) against existing methods.

Main Methods:

Thirty-seven subjects performed a lane-keeping task in a simulated driving environment.
EEG signals were analyzed considering frequency, temporal, and 2-D spatial information.

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A 4-D CNN algorithm was proposed to integrate EEG data and predict driver fatigue levels.

Main Results:

The 4-D CNN demonstrated superior performance compared to 2-D CNN, 3-D CNN, and shallow networks.
Significant improvements were observed: 3.82% in root-mean-square error, 3.45% in error rate, and 11.98% in correlation coefficient.
The algorithm identified distinct theta and alpha brainwave activations associated with fatigue levels in specific brain regions.

Conclusions:

The 4-D CNN offers a powerful approach for analyzing EEG signals to predict driver fatigue.
This study advances the application of deep learning in neuroscience and has potential for real-world safety applications.